mirror of
https://github.com/velocitatem/cvfs.git
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165 lines
11 KiB
TypeScript
165 lines
11 KiB
TypeScript
import type { Document, Submission, InsightsResult } from '@/libs/api';
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const D = (daysAgo: number) => new Date(Date.now() - daysAgo * 86_400_000).toISOString();
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const ROOT_VERSION_ID = 'demo-v1';
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const ML_VERSION_ID = 'demo-v2';
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const BACKEND_VERSION_ID = 'demo-v3';
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export const DEMO_DOC_ID = 'demo-doc-1';
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export const DEMO_DOCUMENTS: Document[] = [
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{
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id: DEMO_DOC_ID,
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title: 'Alex Rivera — Software Engineer',
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description: 'Main CV, ATS-safe baseline',
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owner_id: 'demo-user',
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root_version_id: ROOT_VERSION_ID,
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created_at: D(45),
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updated_at: D(3),
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versions: [
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{
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id: ROOT_VERSION_ID,
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branch_name: 'root',
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version_label: 'v1.0 baseline',
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parent_version_id: null,
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structured_blocks: [
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{ path: 'heading[1]', block_type: 'heading', text: 'Alex Rivera', keywords: [] },
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{ path: 'summary[1]', block_type: 'summary', text: 'Software engineer with 5 years of experience building distributed systems and ML pipelines at scale.', keywords: ['distributed', 'systems', 'machine', 'learning'] },
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{ path: 'heading[2]', block_type: 'heading', text: 'Experience', keywords: [] },
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{ path: 'bullet[1]', block_type: 'bullet', text: 'Led migration of monolithic data pipeline to distributed microservices, reducing p99 latency by 40%.', keywords: ['distributed', 'microservices', 'latency', 'pipeline'] },
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{ path: 'bullet[2]', block_type: 'bullet', text: 'Designed feature flag system used by 50+ engineers across 3 teams.', keywords: ['system', 'design', 'engineers'] },
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{ path: 'heading[3]', block_type: 'heading', text: 'Skills', keywords: [] },
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{ path: 'skills[1]', block_type: 'skills', text: 'Python, Go, TypeScript, SQL, Kubernetes, AWS, PyTorch', keywords: ['python', 'go', 'typescript', 'pytorch', 'kubernetes'] },
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],
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artifact_docx_key: 'demo/demo-cv.docx',
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patches: [],
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public_assets: [],
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created_at: D(45),
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updated_at: D(45),
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},
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{
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id: ML_VERSION_ID,
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branch_name: 'ml-engineer',
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version_label: 'ML-focused variant',
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parent_version_id: ROOT_VERSION_ID,
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structured_blocks: [
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{ path: 'heading[1]', block_type: 'heading', text: 'Alex Rivera', keywords: [] },
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{ path: 'summary[1]', block_type: 'summary', text: 'ML engineer specialising in large-scale PyTorch training pipelines, distributed inference, and production-grade MLOps.', keywords: ['pytorch', 'distributed', 'mlops', 'inference'] },
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{ path: 'heading[2]', block_type: 'heading', text: 'Experience', keywords: [] },
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{ path: 'bullet[1]', block_type: 'bullet', text: 'Contributed PyTorch anomaly detection model achieving 92% precision on production traffic at 2M events/day.', keywords: ['pytorch', 'machine learning', 'production', 'precision'] },
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{ path: 'bullet[2]', block_type: 'bullet', text: 'Built streaming data ingestion system (Kafka + Flink) powering real-time ML feature store.', keywords: ['kafka', 'flink', 'streaming', 'feature store'] },
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{ path: 'heading[3]', block_type: 'heading', text: 'Skills', keywords: [] },
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{ path: 'skills[1]', block_type: 'skills', text: 'PyTorch, Python, Go, Kubernetes, Spark, dbt, AWS SageMaker', keywords: ['pytorch', 'python', 'kubernetes', 'spark', 'sagemaker'] },
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],
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artifact_docx_key: 'demo/demo-cv.docx',
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patches: [
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{ id: 'dp1', target_path: 'summary[1]', operation: 'replace_text', old_value: 'Software engineer…', new_value: 'ML engineer specialising…', created_at: D(30) },
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{ id: 'dp2', target_path: 'skills[1]', operation: 'boost_keyword', old_value: null, new_value: 'PyTorch', created_at: D(30) },
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],
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public_assets: [{
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id: 'demo-asset-1', slug: 'alex-ml', artifact_key: 'public/alex-ml.docx',
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is_public: true, url: '/demo-cv.docx', version_id: ML_VERSION_ID, submission_id: null, created_at: D(20),
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}],
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created_at: D(30),
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updated_at: D(3),
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},
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{
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id: BACKEND_VERSION_ID,
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branch_name: 'backend-engineer',
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version_label: 'Backend-focused variant',
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parent_version_id: ROOT_VERSION_ID,
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structured_blocks: [
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{ path: 'heading[1]', block_type: 'heading', text: 'Alex Rivera', keywords: [] },
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{ path: 'summary[1]', block_type: 'summary', text: 'Backend engineer focused on high-throughput API design, distributed systems, and reliability engineering.', keywords: ['backend', 'api', 'distributed', 'reliability'] },
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{ path: 'bullet[1]', block_type: 'bullet', text: 'Led migration to microservices, reducing p99 latency by 40% under 10k RPS sustained load.', keywords: ['microservices', 'latency', 'rps', 'distributed'] },
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{ path: 'skills[1]', block_type: 'skills', text: 'Go, Python, PostgreSQL, Redis, gRPC, Kubernetes, AWS', keywords: ['go', 'postgresql', 'redis', 'grpc', 'kubernetes'] },
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],
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artifact_docx_key: 'demo/demo-cv.docx',
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patches: [
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{ id: 'dp3', target_path: 'summary[1]', operation: 'replace_text', old_value: 'Software engineer…', new_value: 'Backend engineer…', created_at: D(25) },
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],
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public_assets: [],
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created_at: D(25),
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updated_at: D(10),
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},
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],
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},
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];
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export const DEMO_SUBMISSIONS: Submission[] = [
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{
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id: 'ds1', version_id: ML_VERSION_ID, company_name: 'Anthropic', role_title: 'ML Research Engineer',
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job_url: null, job_description: null, status: 'pending_review', created_at: D(18),
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suggestions: [
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{ id: 's1', target_path: 'summary[1]', operation: 'boost_keyword', proposed_text: 'constitutional ai', rationale: 'Highlight alignment research experience', accepted: true, metadata_json: { confidence: 0.82 } },
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{ id: 's2', target_path: 'bullet[1]', operation: 'replace_text', proposed_text: 'Built distributed PyTorch training pipeline handling constitutional AI fine-tuning at scale.', rationale: 'Align with Anthropic stack', accepted: true, metadata_json: { confidence: 0.74 } },
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],
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},
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{
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id: 'ds2', version_id: ML_VERSION_ID, company_name: 'Google DeepMind', role_title: 'Senior ML Engineer',
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job_url: null, job_description: null, status: 'pending_review', created_at: D(14),
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suggestions: [
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{ id: 's3', target_path: 'skills[1]', operation: 'boost_keyword', proposed_text: 'JAX', rationale: 'DeepMind uses JAX heavily', accepted: true, metadata_json: { confidence: 0.71 } },
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{ id: 's4', target_path: 'bullet[2]', operation: 'replace_text', proposed_text: 'Built large-scale streaming pipeline underpinning real-time feature store for JAX model serving.', rationale: 'Add JAX context', accepted: true, metadata_json: { confidence: 0.68 } },
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],
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},
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{
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id: 'ds3', version_id: ML_VERSION_ID, company_name: 'OpenAI', role_title: 'Research Engineer',
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job_url: null, job_description: null, status: 'published', created_at: D(10),
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suggestions: [
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{ id: 's5', target_path: 'summary[1]', operation: 'replace_text', proposed_text: 'ML engineer with track record in large-scale training infrastructure and RLHF pipelines.', rationale: 'OpenAI focus on RLHF', accepted: true, metadata_json: { confidence: 0.77 } },
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],
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},
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{
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id: 'ds4', version_id: ML_VERSION_ID, company_name: 'Meta AI', role_title: 'ML Infrastructure Engineer',
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job_url: null, job_description: null, status: 'archived', created_at: D(22),
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suggestions: [
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{ id: 's6', target_path: 'bullet[1]', operation: 'boost_keyword', proposed_text: 'PyTorch', rationale: 'Meta maintains PyTorch', accepted: true, metadata_json: { confidence: 0.55 } },
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{ id: 's7', target_path: 'summary[1]', operation: 'suppress_block', proposed_text: null, rationale: 'Summary too generic', accepted: false, metadata_json: { confidence: 0.3 } },
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],
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},
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{
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id: 'ds5', version_id: BACKEND_VERSION_ID, company_name: 'Stripe', role_title: 'Senior Backend Engineer',
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job_url: null, job_description: null, status: 'pending_review', created_at: D(8),
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suggestions: [
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{ id: 's8', target_path: 'bullet[1]', operation: 'replace_text', proposed_text: 'Led migration to microservices achieving 99.99% uptime across Stripe-scale payment processing.', rationale: 'Emphasise reliability', accepted: true, metadata_json: { confidence: 0.79 } },
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],
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},
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{
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id: 'ds6', version_id: BACKEND_VERSION_ID, company_name: 'Cloudflare', role_title: 'Staff Engineer',
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job_url: null, job_description: null, status: 'archived', created_at: D(20),
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suggestions: [
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{ id: 's9', target_path: 'skills[1]', operation: 'boost_keyword', proposed_text: 'Rust', rationale: 'Cloudflare uses Rust', accepted: true, metadata_json: { confidence: 0.4 } },
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],
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},
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];
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export const DEMO_INSIGHTS: InsightsResult = {
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total_submissions: 6,
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positive_count: 4,
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positive_rate: 0.667,
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has_data: true,
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operation_impact: [
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{ operation: 'replace_text', total: 5, positive: 4, rate: 0.8 },
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{ operation: 'boost_keyword', total: 5, positive: 3, rate: 0.6 },
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{ operation: 'suppress_block', total: 1, positive: 0, rate: 0.0 },
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],
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top_positive_keywords: [
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{ keyword: 'pytorch', positive_count: 4, negative_count: 1, lift: 4.0 },
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{ keyword: 'distributed', positive_count: 3, negative_count: 0, lift: 3.0 },
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{ keyword: 'pipeline', positive_count: 3, negative_count: 1, lift: 3.0 },
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{ keyword: 'scale', positive_count: 3, negative_count: 1, lift: 3.0 },
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{ keyword: 'reliability', positive_count: 2, negative_count: 0, lift: 2.0 },
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{ keyword: 'inference', positive_count: 2, negative_count: 0, lift: 2.0 },
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],
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top_negative_keywords: [
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{ keyword: 'generic', positive_count: 0, negative_count: 2, lift: 0.0 },
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{ keyword: 'suppress', positive_count: 0, negative_count: 1, lift: 0.0 },
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],
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section_impact: [
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{ section: 'summary', positive_rate: 0.83, count: 6 },
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{ section: 'bullet', positive_rate: 0.75, count: 4 },
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{ section: 'skills', positive_rate: 0.5, count: 4 },
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],
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};
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